Face Recognition Using The Subspace and Deep Learning Algorithms For Cases of Sufficient and Insufficient Data

Year 2024, , 1111 - 1121, 31.12.2024

Serkan Keser

https://doi.org/10.17798/bitlisfen.1518498

Abstract

In face recognition, the distance criterion significantly influences the recognition rate. Misclassified test signals can be accurately reassigned to the correct class using various distance measures and the nearest neighbor algorithm. This study uniquely explores the recognition performance of DCVA, Fisherface subspace classifiers, and Convolutional Neural Network (CNN) in face recognition, an aspect not thoroughly explored in the literature. Accordingly, this study introduces a Discriminative Common Vector-based (DCVA) algorithm utilizing various distance measures for face recognition for the first time. Additionally, the Fisherface-based algorithm uses different distance measures and nearest neighbors. Experiments were conducted on three different face databases. The images were downsampled to simulate both sufficient and insufficient data conditions. Experimental results indicate that the Correlation distance measure generally outperforms the Euclidean distance for the DCVA and Fisherface-KNN algorithms under both data conditions. The Fisherface-KNN algorithm surpasses the classical Fisherface in performance for various distance measures and nearest neighbor numbers and yields better recognition rates than the DCVA algorithm in sufficient data conditions. Moreover, while DCVA and Fisherface-KNN achieved superior results for two smaller face databases, CNN demonstrated better performance for larger databases.

Keywords

Face recognition, Fisherface-KNN, DCVA, CNN

Ethical Statement

The study is complied with research and publication ethics.

References

• S. Jain and D. Bhati, “Face recognition using ANN with reduce feature by PCA in wavelet domain,” International Journal of Scientific Engineering and Technology, vol. 2, no. 6, pp. 595–599, 2013.
• M. A. Abuzneid and A. Mahmood, “Enhanced human face recognition using LBPH descriptor, multi-KNN, and back-propagation neural network,” IEEE Access, vol. 6, pp. 20641–20651, 2018.
• H. S. Dadi and G. M. Pillutla, “Improved face recognition rate using HOG features and SVM classifier,” IOSR Journal of Electronics and Communication Engineering, vol. 11, no. 4, pp. 34–44, 2016.
• M. Anggo and L. Arapu, “Face recognition using fisherface method,” in Journal of Physics: Conference Series, vol. 1028, no. 1, p. 012119. IOP Publishing, 2018.
• X. He, S. Yan, Y. Hu, P. Niyogi, and H. J. Zhang, “Face recognition using laplacianfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328–340, 2005.
• S. Ergin and M. B. Gulmezoglu, “Face recognition based on face partitions using common vector approach,” in 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP), 2008, pp. 624–628.
• H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, “Discriminative common vectors for face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 4–13, 2005.
• A. Martinez, “Fisherfaces,” Scholarpedia, vol. 6, no. 2, p. 4282, 2011.
• N. Kumar., P. Belhumeur and S. Nayar. “Facetracer: A search engine for large collections of images with faces”, In Computer Vision–ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, Proceedings, Part IV 10, pp. 340-353, 2008.
• K. Özkan and E. Seke, “Image denoising using common vector approach,” IET Image Processing, vol. 9, no. 8, pp. 709–715, 2015.
• S. Sadıç and M. B. Gülmezoğlu, “Common vector approach and its combination with GMM for text-independent speaker recognition,” Expert Systems with Applications, vol. 38, no. 9, pp. 11394–11400, 2011.
• Ş. Işık, K. Özkan, and Ö. N. Gerek, “CVABS: moving object segmentation with common vector approach for videos,” IET Computer Vision, vol. 13, no. 8, pp. 719–729, 2019.
• S. Günal, S. Ergin, and Ö. N. Gerek, “Spam E-mail recognition by subspace analysis,” in INISTA – International Symposium on Innovations in Intelligent Systems and Applications, 2005, pp. 307–310.
• M. L. Zhang and Z. H. Zhou, “ML-KNN: A lazy learning approach to multi-label learning,” Pattern Recognition, vol. 40, no. 7, pp. 2038–2048, 2007.
• P. Miller and J. Lyle, “The effect of distance measures on the recognition rates of PCA and LDA based facial recognition,” in Digital Image Processing, 2008.
• M. S. Ahuja and S. Chhabra, “Effect of distance measures in PCA based face recognition,” International Journal of Enterprise Computing and Business Systems, vol. 1, no. 2, p. 2230–8849, 2011.
• H. Saadatfar, S. Khosravi, J. H. Joloudari, A. Mosavi, and S. Shamshirband, “A new K-nearest neighbors classifier for big data based on efficient data pruning,” Mathematics, vol. 8, no. 2, p. 286, 2020.
• A. G. Hatzimichailidis, G. A. Papakostas, and V. G. Kaburlasos, “A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems,” International Journal of Intelligent Systems, vol. 27, no. 4, pp. 396–409, 2012.
• V. Perlibakas, “Distance measures for PCA-based face recognition,” Pattern Recognition Letters, vol. 25, no. 6, pp. 711–724, 2004.
• P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997.
• M. Anggo and L. Arapu, “Face recognition using fisherface method,” in Journal of Physics: Conference Series, vol. 1028, no. 1, p. 012119. IOP Publishing, 2018.
• K. Chomboon, P. Chujai, P. Teerarassamee, K. Kerdprasop, and N. Kerdprasop, “An empirical study of distance metrics for k-nearest neighbor algorithm,” in Proceedings of the 3rd International Conference on Industrial Application Engineering, 2015, pp. 280–285.
• Y. Xie, Y. Wang, A. Nallanathan, and L. Wang, “An improved K-nearest-neighbor indoor localization method based on spearman distance,” IEEE Signal Processing Letters, vol. 23, no. 3, pp. 351–355, 2016.
• M. B. Gülmezoğlu, V. Dzhafarov, R. Edizkan, and A. Barkana, “The common vector approach and its comparison with other subspace methods in case of sufficient data,” Computer Speech & Language, vol. 21, no. 2, pp. 266–281, 2007.
• M. V. Valueva, N. N. Nagornov, P. A. Lyakhov, G. V. Valuev, and N. I. Chervyakov, “Application of the residue number system to reduce hardware costs of the convolutional neural network implementation,” Mathematics and Computers in Simulation, 2020.
• L. Shang, Q. Yang, J. Wang, S. Li, and W. Lei, “Detection of rail surface defects based on CNN image recognition and classification,” in 2018 20th International Conference on Advanced Communication Technology (ICACT), 2018, pp. 45–51.
• Y. Fan, X. Lu, D. Li, and Y. Liu, “Video-based emotion recognition using CNN-RNN and C3D hybrid networks,” in Proceedings of the 18th ACM International Conference on Multimodal Interaction, 2016, pp. 445–450.
• M. Zhang, W. Li, and Q. Du, “Diverse region-based CNN for hyperspectral image classification,” IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2623–2634, 2018.
• B. Kayalibay, G. Jensen, and P. van der Smagt, “CNN-based segmentation of medical imaging data,” arXiv preprint arXiv:1701.03056, 2017.
• J. Thomas, T. Maszczyk, N. Sinha, T. Kluge, and J. Dauwels, “Deep learning-based classification for brain-computer interfaces,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 234–239.
• H. Ben Fredj, S. Bouguezzi, and C. Souani, “Face recognition in unconstrained environment with CNN,” The Visual Computer, vol. 37, no. 2, pp. 217–226, 2021.
• S. Sharma, K. Shanmugasundaram, and S. K. Ramasamy, “FAREC—CNN based efficient face recognition technique using Dlib,” in 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2016, pp. 192–195.
• M. Arsenovic, S. Sladojevic, A. Anderla, and D. Stefanovic, “FaceTime—Deep learning based face recognition attendance system,” in 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), 2017, pp. 000053–000058.
• S. Saxena and J. Verbeek, “Heterogeneous face recognition with CNNs,” in European Conference on Computer Vision, 2016, pp. 483–491.
• K. C. Lee, J. Ho, and D. J. Kriegman, “Acquiring linear subspaces for face recognition under variable lighting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 684–698, 2005.
• S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1–6.
• S. Khare and M. Totaro, “Ensemble learning for detecting attacks and anomalies in IoT smart home,” in Proceedings of the 2020 3rd International Conference on Data Intelligence and Security (ICDIS), 2020, pp. 56–63.
• N. Butt, A. Shahid, K. N. Qureshi, S. Haider, A. O. Ibrahim, F. Binzagr, and N. Arshad, “Intelligent deep learning for anomaly-based intrusion detection in IoT smart home networks,” Mathematics, vol. 10, p. 4598, 2022.
• E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, “A supervised intrusion detection system for smart home IoT devices,” IEEE Internet of Things Journal, vol. 6, pp. 9042–9053, 2019.
• C. Stolojescu-Crisan, C. Crisan, and B. P. Butunoi, “An IoT-based smart home automation system,” Sensors, vol. 21, p. 3784, 2021.
• M. R. Dhobale, R. Y. Biradar, R. R. Pawar, and S. A. Awatade, “Smart home security system using IoT, face recognition, and Raspberry Pi,” IEEE Int. J. Comput. Appl., vol. 176, pp. 45–47, 2020.
• H. Kumar and P. Padmavati, “Face recognition using SIFT by varying distance calculation matching method,” International Journal of Computer Applications, vol. 47, no. 3, pp. 20–26, 2012.